1
|
Bellemo V, Kumar Das A, Sreng S, Chua J, Wong D, Shah J, Jonas R, Tan B, Liu X, Xu X, Tan GSW, Agrawal R, Ting DSW, Yong L, Schmetterer L. Optical coherence tomography choroidal enhancement using generative deep learning. NPJ Digit Med 2024; 7:115. [PMID: 38704440 PMCID: PMC11069520 DOI: 10.1038/s41746-024-01119-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/23/2024] [Indexed: 05/06/2024] Open
Abstract
Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts' ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson's correlations of 0.97 [95% CI: 0.96-0.98], 0.97 [0.95-0.98], 0.95 [0.92-0.98], and 0.87 [0.83-0.91], with intra-class correlation values of 0.99 [0.98-0.99], 0.98 [0.98-0.99], and 0.95 [0.96-0.98], 0.93 [0.91-0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions.
Collapse
Affiliation(s)
- Valentina Bellemo
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
| | - Ankit Kumar Das
- Institute of High Performance Computing, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Syna Sreng
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
| | - Jacqueline Chua
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Damon Wong
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Centre for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Janika Shah
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Rahul Jonas
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department Ophthalmology, Cologne, Germany
| | - Bingyao Tan
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department Ophthalmology, Cologne, Germany
| | - Xinyu Liu
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Xinxing Xu
- Institute of High Performance Computing, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Rupesh Agrawal
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore School of Chemical and Biomedical Engineering, Nanyang Technological University (NTU), Singapore, Singapore
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Liu Yong
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore.
- Institute of High Performance Computing, Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore.
| | - Leopold Schmetterer
- Singapore Eye Research Institute, National Eye Centre, Singapore, Singapore.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
- SERI-NTU Advanced Ocular Engineering (STANCE) Program, Singapore, Singapore.
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore.
- Centre for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore.
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria.
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland.
| |
Collapse
|
2
|
Thakur S, Dinh LL, Lavanya R, Quek TC, Liu Y, Cheng CY. Use of artificial intelligence in forecasting glaucoma progression. Taiwan J Ophthalmol 2023; 13:168-183. [PMID: 37484617 PMCID: PMC10361424 DOI: 10.4103/tjo.tjo-d-23-00022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/03/2023] [Indexed: 07/25/2023] Open
Abstract
Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.
Collapse
Affiliation(s)
- Sahil Thakur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Linh Le Dinh
- Institute of High Performance Computing, The Agency for Science, Technology and Research, Singapore
| | - Raghavan Lavanya
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yong Liu
- Institute of High Performance Computing, The Agency for Science, Technology and Research, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| |
Collapse
|
3
|
Sreejith Kumar AJ, Chong RS, Crowston JG, Chua J, Bujor I, Husain R, Vithana EN, Girard MJA, Ting DSW, Cheng CY, Aung T, Popa-Cherecheanu A, Schmetterer L, Wong D. Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma. JAMA Ophthalmol 2022; 140:974-981. [PMID: 36048435 PMCID: PMC9437828 DOI: 10.1001/jamaophthalmol.2022.3375] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train such DL networks for glaucoma detection. Objective To assess whether generative models can synthesize circumpapillary optic nerve head OCT images of normal and glaucomatous eyes and determine the usability of synthetic images for training DL models for glaucoma detection. Design, Setting, and Participants Progressively growing generative adversarial network models were trained to generate circumpapillary OCT scans. Image gradeability and authenticity were evaluated on a clinical set of 100 real and 100 synthetic images by 2 clinical experts. DL networks for glaucoma detection were trained with real or synthetic images and evaluated on independent internal and external test data sets of 140 and 300 real images, respectively. Main Outcomes and Measures Evaluations of the clinical set between the experts were compared. Glaucoma detection performance of the DL networks was assessed using area under the curve (AUC) analysis. Class activation maps provided visualizations of the regions contributing to the respective classifications. Results A total of 990 normal and 862 glaucomatous eyes were analyzed. Evaluations of the clinical set were similar for gradeability (expert 1: 92.0%; expert 2: 93.0%) and authenticity (expert 1: 51.8%; expert 2: 51.3%). The best-performing DL network trained on synthetic images had AUC scores of 0.97 (95% CI, 0.95-0.99) on the internal test data set and 0.90 (95% CI, 0.87-0.93) on the external test data set, compared with AUCs of 0.96 (95% CI, 0.94-0.99) on the internal test data set and 0.84 (95% CI, 0.80-0.87) on the external test data set for the network trained with real images. An increase in the AUC for the synthetic DL network was observed with the use of larger synthetic data set sizes. Class activation maps showed that the regions of the synthetic images contributing to glaucoma detection were generally similar to that of real images. Conclusions and Relevance DL networks trained with synthetic OCT images for glaucoma detection were comparable with networks trained with real images. These results suggest potential use of generative models in the training of DL networks and as a means of data sharing across institutions without patient information confidentiality issues.
Collapse
Affiliation(s)
- Ashish Jith Sreejith Kumar
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.,Institute for Infocomm Research, A*STAR, Singapore
| | - Rachel S Chong
- Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Jonathan G Crowston
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Jacqueline Chua
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Inna Bujor
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Rahat Husain
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Eranga N Vithana
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Michaël J A Girard
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Alina Popa-Cherecheanu
- Carol Davila University of Medicine and Pharmacy, Bucharest, Romania.,Emergency University Hospital, Department of Ophthalmology, Bucharest, Romania
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Academic Clinical Program, Duke-NUS Medical School, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.,Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland.,Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria.,School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore.,Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria.,Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
| | - Damon Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore.,School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore
| |
Collapse
|
4
|
Lim JS, Hong M, Lam WST, Zhang Z, Teo ZL, Liu Y, Ng WY, Foo LL, Ting DSW. Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2022; 33:174-187. [PMID: 35266894 DOI: 10.1097/icu.0000000000000846] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.
Collapse
Affiliation(s)
- Jane S Lim
- Singapore National Eye Centre, Singapore Eye Research Institute
| | | | - Walter S T Lam
- Yong Loo Lin School of Medicine, National University of Singapore
| | - Zheting Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University
| | - Zhen Ling Teo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Yong Liu
- National University of Singapore, DukeNUS Medical School, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
| |
Collapse
|
5
|
A Tool for High-Resolution Volumetric Optical Coherence Tomography by Compounding Radial-and Linear Acquired B-Scans Using Registration. SENSORS 2022; 22:s22031135. [PMID: 35161880 PMCID: PMC8839892 DOI: 10.3390/s22031135] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/22/2022] [Accepted: 01/28/2022] [Indexed: 12/02/2022]
Abstract
Optical coherence tomography (OCT) is a medical imaging modality that is commonly used to diagnose retinal diseases. In recent years, linear and radial scanning patterns have been proposed to acquire three-dimensional OCT data. These patterns show differences in A-scan acquisition density across the generated volumes, and thus differ in their suitability for the diagnosis of retinal diseases. While radial OCT volumes exhibit a higher A-scan sampling rate around the scan center, linear scans contain more information in the peripheral scan areas. In this paper, we propose a method to combine a linearly and radially acquired OCT volume to generate a single compound volume, which merges the advantages of both scanning patterns to increase the information that can be gained from the three-dimensional OCT data. We initially generate 3D point clouds of the linearly and radially acquired OCT volumes and use an Iterative Closest Point (ICP) variant to register both volumes. After registration, the compound volume is created by selectively exploiting linear and radial scanning data, depending on the A-scan density of the individual scans. Fusing regions from both volumes with respect to their local A-scan sampling density, we achieve improved overall anatomical OCT information in a high-resolution compound volume. We demonstrate our method on linear and radial OCT volumes for the visualization and analysis of macular holes and the surrounding anatomical structures.
Collapse
|
6
|
You A, Kim JK, Ryu IH, Yoo TK. Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. EYE AND VISION (LONDON, ENGLAND) 2022; 9:6. [PMID: 35109930 PMCID: PMC8808986 DOI: 10.1186/s40662-022-00277-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 01/11/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has demonstrated remarkable performance in image synthesis and image-to-image translation. The adoption of GAN for medical imaging is increasing for image generation and translation, but it is not familiar to researchers in the field of ophthalmology. In this work, we present a literature review on the application of GAN in ophthalmology image domains to discuss important contributions and to identify potential future research directions. METHODS We performed a survey on studies using GAN published before June 2021 only, and we introduced various applications of GAN in ophthalmology image domains. The search identified 48 peer-reviewed papers in the final review. The type of GAN used in the analysis, task, imaging domain, and the outcome were collected to verify the usefulness of the GAN. RESULTS In ophthalmology image domains, GAN can perform segmentation, data augmentation, denoising, domain transfer, super-resolution, post-intervention prediction, and feature extraction. GAN techniques have established an extension of datasets and modalities in ophthalmology. GAN has several limitations, such as mode collapse, spatial deformities, unintended changes, and the generation of high-frequency noises and artifacts of checkerboard patterns. CONCLUSIONS The use of GAN has benefited the various tasks in ophthalmology image domains. Based on our observations, the adoption of GAN in ophthalmology is still in a very early stage of clinical validation compared with deep learning classification techniques because several problems need to be overcome for practical use. However, the proper selection of the GAN technique and statistical modeling of ocular imaging will greatly improve the performance of each image analysis. Finally, this survey would enable researchers to access the appropriate GAN technique to maximize the potential of ophthalmology datasets for deep learning research.
Collapse
Affiliation(s)
- Aram You
- School of Architecture, Kumoh National Institute of Technology, Gumi, Gyeongbuk, South Korea
| | - Jin Kuk Kim
- B&VIIT Eye Center, Seoul, South Korea
- VISUWORKS, Seoul, South Korea
| | - Ik Hee Ryu
- B&VIIT Eye Center, Seoul, South Korea
- VISUWORKS, Seoul, South Korea
| | - Tae Keun Yoo
- B&VIIT Eye Center, Seoul, South Korea.
- Department of Ophthalmology, Aerospace Medical Center, Republic of Korea Air Force, 635 Danjae-ro, Namil-myeon, Cheongwon-gun, Cheongju, Chungcheongbuk-do, 363-849, South Korea.
| |
Collapse
|
7
|
Kumar H, Goh KL, Guymer RH, Wu Z. A clinical perspective on the expanding role of artificial intelligence in age-related macular degeneration. Clin Exp Optom 2022; 105:674-679. [PMID: 35073498 DOI: 10.1080/08164622.2021.2022961] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
In recent years, there has been intense development of artificial intelligence (AI) techniques, which have the potential to improve the clinical management of age-related macular degeneration (AMD) and facilitate the prevention of irreversible vision loss from this condition. Such AI techniques could be used as clinical decision support tools to: (i) improve the detection of AMD by community eye health practitioners, (ii) enhance risk stratification to enable personalised monitoring strategies for those with the early stages of AMD, and (iii) enable early detection of signs indicative of possible choroidal neovascularisation allowing triaging of patients requiring urgent review. This review discusses the latest developments in AI techniques that show promise for these tasks, as well as how they may help in the management of patients being treated for choroidal neovascularisation and in accelerating the discovery of new treatments in AMD.
Collapse
Affiliation(s)
- Himeesh Kumar
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Victoria, Australia
| | - Kai Lyn Goh
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Victoria, Australia
| | - Robyn H Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Victoria, Australia
| | - Zhichao Wu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Victoria, Australia
| |
Collapse
|
8
|
Bunod R, Augstburger E, Brasnu E, Labbe A, Baudouin C. [Artificial intelligence and glaucoma: A literature review]. J Fr Ophtalmol 2022; 45:216-232. [PMID: 34991909 DOI: 10.1016/j.jfo.2021.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 11/18/2021] [Indexed: 11/26/2022]
Abstract
In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.
Collapse
Affiliation(s)
- R Bunod
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France.
| | - E Augstburger
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France
| | - E Brasnu
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France
| | - A Labbe
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
| | - C Baudouin
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
| |
Collapse
|
9
|
Lazaridis G, Mohamed-Noriega J, Aguilar-Munoa S, Suzuki K, Nomoto H, Garway-Heath DF. Imaging Outcomes in Clinical Trials of Treatments for Glaucoma. Ophthalmology 2020; 128:1240-1242. [PMID: 33373618 DOI: 10.1016/j.ophtha.2020.11.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/17/2020] [Accepted: 11/25/2020] [Indexed: 10/22/2022] Open
Affiliation(s)
- Georgios Lazaridis
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom; Centre for Medical Image Computing, University College London, London, United Kingdom.
| | - Jibran Mohamed-Noriega
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom; Departamento de Oftalmología, Hospital Universitario, UANL, Monterrey, México
| | | | - Katsuyoshi Suzuki
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Hiroki Nomoto
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - David F Garway-Heath
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | | |
Collapse
|